A consequence of the Moore Nielsen prediction is the phenomenon known as Data Gravity: big data is hard to move around, much easier for the smaller applications to come to it. Consider this: it took mankind over 2000 years to produce 2 Exabytes (2×1018 bytes) of data until 2012; now we produce this much in a day! The rate will go up from here. With data production far exceeding the capacity of the Network, particularly at the Edge, there is only one way to cope, which I call the three mega trends in networking and (big) data in Cloud computing scaled to IoT, or as some say, Fog computing:

Dramatic growth in the applications specialized and optimized for analytics at the Edge: Big Data is hard to move around (data gravity), cannot move data fast enough to the analytics, therefore we need to move the analytics to the data. This will cause a dramatic growth in applications, specialized and optimized for analytics at the edge. Yes, our devices have gotten smarter, yes P2P traffic has become largest portion of Internet traffic, and yes M2M has arrived as the Internet of Things, there is no way to make progress but making the devices smarter, safer and, of course, better connected.

Dramatic growth in the computational complexity to ETL (extract-transform-load) essential data from the Edge to be data-warehoused at the Core: Currently most open standards and open source efforts are buying us some time to squeeze as much information in as little time as possible via limited connection paths to billions of devices and soon enough we will realize there is a much more pragmatic approach to all of this. A jet engine produces more than 20 Terabytes of data for an hour of flight. Imagine what computational complexity we already have that boils that down to routing and maintenance decisions in such complex machines. Imagine the consequences of ignoring such capability, which can already be made available at rather trivial costs.

The drive to instrument the data to be “open” rather than “closed”, with all the information we create, and all of its associated ownership and security concerns addressed: Open Data challenges have already surfaced, there comes a time when we begin to realize that an Open Data interface and guarantees about its availability and privacy need to be made and enforced. This is what drives the essential tie today between Public, Private and Hybrid cloud adoption (nearly one third each) and with the ever-growing amount of data at the Edge, the issue of who “owns” it and how is access “controlled” to it, become ever more relevant and important. At the end of the day, the producer/owner of the data must be in charge of its destiny, not some gatekeeper or web farm. This should not be any different that the very same rules that govern open source or open standards.

Last week I addressed these topics at the IEEE Cloud event at Boston University with wonderful colleagues from BU, Cambridge, Carnegie Mellon, MIT, Stanford and other researchers, plus of course, industry colleagues and all the popular, commercial web farms today. I was pleasantly surprised to see not just that the first two are top-of-mind already, but that the third one has emerged and is actually recognized. We have just started to sense the importance of this third wave, with huge implications in Cloud compute. My thanks to Azer Bestavros and Orran Krieger (Boston University), Mahadev Satyanarayanan (Carnegie Mellon University) and Michael Stonebraker (MIT) for the outstanding drive and leadership in addressing these challenges. I found Project Olive intriguing. We are happy to co-sponsor the BU Public Cloud Project, and most importantly, as we just wrapped up EclipseCon 2014 this week, very happy to see we are already walking the talk with Project Krikkit in Eclipse M2M. I made a personal prediction last week: just as most Cloud turned out to be Open Source, IoT software will all be Open Source. Eventually. The hard part is the Data, or should I say, Data Gravity…

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